Abstract
BackgroundCT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. A blood test that could manage these limitations would be useful, but development of such tests has been impaired by variations in blood collection that may lead to poor reproducibility across populations.ResultsBlood-based proteomic profiles were generated with SOMAscan technology, which measured 1033 proteins. First, preanalytic variability was evaluated with Sample Mapping Vectors (SMV), which are panels of proteins that detect confounders in protein levels related to sample collection. A subset of well collected serum samples not influenced by preanalytic variability was selected for discovery of lung cancer biomarkers. The impact of sample collection variation on these candidate markers was tested in the subset of samples with higher SMV scores so that the most robust markers could be used to create disease classifiers. The discovery sample set (n = 363) was from a multi-center study of 94 non-small cell lung cancer (NSCLC) cases and 269 long-term smokers and benign pulmonary nodule controls. The analysis resulted in a 7-marker panel with an AUC of 0.85 for all cases (68% adenocarcinoma, 32% squamous) and an AUC of 0.93 for squamous cell carcinoma in particular. This panel was validated by making blinded predictions in two independent cohorts (n = 138 in the first validation and n = 135 in the second). The model was recalibrated for a panel format prior to unblinding the second cohort. The AUCs overall were 0.81 and 0.77, and for squamous cell tumors alone were 0.89 and 0.87. The estimated negative predictive value for a 15% disease prevalence was 93% overall and 99% for squamous lung tumors. The proteins in the classifier function in destruction of the extracellular matrix, metabolic homeostasis and inflammation.ConclusionsSelecting biomarkers resistant to sample processing variation led to robust lung cancer biomarkers that performed consistently in independent validations. They form a sensitive signature for detection of lung cancer, especially squamous cell histology. This non-invasive test could be used to improve the positive predictive value of CT screening, with the potential to avoid invasive evaluation of nonmalignant pulmonary nodules.
Highlights
CT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers
The result of these studies was the development of a set of Sample Mapping Vectors (SMV) which allowed us to quantify the magnitude of confounding pre-analytical variation introduced by sample collection differences
HSP90 levels in serum are affected by cell lysis during sample processing and intracellular protein contamination leaking into serum, causing a relative shift in the measured concentration
Summary
CT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. The low (4%) positive predictive value (PPV) of CT screening in the NLST cohort leads to a large number of unnecessary follow-up procedures, including surgery for benign nodules, as was first reported in the Pittsburgh Lung Screening Study (PLuSS) and later in the NLST [6,7]. SQ are the most common NSCLC histology missed by CT [6,15], perhaps because a central tumor location obscures CT detection and because SQ tumors tend to have a rapid growth rate that can lead to diagnosis as interval cancers between scans. In a Taiwan population-based registry of over 33,000 lung cancer patients, those with SQ tumors had shorter median survival times than adenocarcinoma (AD) tumors [16]
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